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Abstract While economic sociology research and theory argue that excessive network embeddedness depresses competition in illegal markets, prior research does not examine how distinct types of embeddedness may have asymmetric effects on the diversity of purchasing behavior—the range of illegal goods that buyers typically purchase. This study considers how network embeddedness can positively or negatively affect drug purchasing diversity in online drug markets by referring buyers to new vendors or “locking” buyers into recurrent trade for the same products. We analyze novel network data on 16,847 illegal drug exchanges between 7205 actors on one online illegal drug market. Consistent with hypothesized network asymmetry, buyers are more likely to purchase a new type of drug when the transaction is part of an indirect network referral. Although histories of exchange increase the overall frequency of drug purchasing, they are associated with decreases in new drug-type purchases. In the aggregate, these processes either contribute to an integrated market where buyers purchase multiple drugs from multiple vendors (in the case of referrals) or a fragmented market characterized by recurrent trade from the same vendors for the same substances (in the case of repeated trade). We discuss the implications of these findings for research on embeddedness, illegal markets, risky exchange, and drug policy.
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This content will become publicly available on January 10, 2026
Closing Time: The Impact of Transitivity on Organizational Instant Messaging
We conducted an exploratory study of 25,260 instant messages from 14 interdisciplinary team networks focused on digital innovation with advanced machine learning technologies. We seek to contribute to theory on network embeddedness in such teams and find that the embeddedness of a message sender influences the messaging activity in teams. However, we further find that the influence of homophily vary depending on the topology of the network, and can contribute to the stratification of these network teams.
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- Award ID(s):
- 2202693
- PAR ID:
- 10598429
- Publisher / Repository:
- ScholarSpace https://scholarspace.manoa.hawaii.edu/communities/aaeec9ed-5368-44e3-88e5-33ea62366840
- Date Published:
- Format(s):
- Medium: X
- Location:
- Waikoloa Village, University of Hawaii
- Sponsoring Org:
- National Science Foundation
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